陈静. 2019. 一种基于可预报性的暴雨预报评分新方法Ⅰ:中国暴雨可预报性综合指数[J]. 气象学报, (0):-, doi:10.11676/qxxb2019.002
一种基于可预报性的暴雨预报评分新方法Ⅰ:中国暴雨可预报性综合指数
A New Method for Heavy Rainfall Forecast Verification Based on PredictabilityⅠ:Synthetic Predictability Index of Heavy Rainfall in China
投稿时间:2017-12-11  修订日期:2018-05-30
DOI:10.11676/qxxb2019.002
中文关键词:  中国暴雨 暴雨气候频率 暴雨面积比率 数值模式暴雨评分 可预报性综合指数
英文关键词:Heavy rainfall over china, Climate frequency, Area ratio, Threat Score of heavy rainfall of numerical model, Synthetic Predictability Index of Heavy Rainfall
基金项目:中国气象局气象预报业务关键技术发展专项(YBGJXM(2017)06),国家科技支撑计划项目(2015BAC03B01)
作者单位E-mail
陈静 国家数值预报中心 chenj@cma.gov.cn 
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中文摘要:
      针对当前暴雨检验方法采用二分类事件检验方法存在的双重惩罚导致评分过低,没有考虑到中国暴雨可预报性时空分布不均的问题,不便于对比分析不同区域暴雨预报能力等问题,为了发展基于可预报性的新型暴雨评分方法,在综合分析影响预报员暴雨预报信心的主要因素(暴雨气候统计特征、天气影响系统运动尺度特征及数值模式预报能力等)基础上,利用2008—2016年4—10月国家气象信息中心5km*5km分辨率的多源降水融合格点分析资料、站点降水观测资料和国家级业务区域模式降水预报资料以及扩展空间暴雨样本统计方法,构建了一种新型的中国暴雨可预报性综合指数(Synthetic Predictability Index of Heavy Rainfall,以下简称SPI)数学模型,以定量描述中国各区域的暴雨可预报性特征。SPI数学模型由暴雨气候频率、暴雨面积比率和模式暴雨预报成功指数(Threat Score,TS)三个分量组成,计算了2008—2016年4—10月SPI的三个分量及其时空变化特征。分析结果显示: 暴雨面积比率对SPI的时间和空间变化影响最大,两者偏相关系数大于0.9;其次是暴雨气候频率的影响,两者偏相关系数值为0.8左右;第三是模式暴雨预报TS评分的影响,两者的偏相关系数为0.7左右。分析还发现,SPI大值区随季节而变化,空间分布不均匀:4月至5月,可预报性大值区主要分布在华南地区;6月至7月,主要分布在江淮流域; 7月中旬至8月,大值中心从江淮北部移到华北和东北地区;9月,副热带高压南撤,大值中心也相应南撤。中国暴雨可预报性综合指数SPI模型为发展基于可预报性的暴雨预报检验评分奠定了较好的基础。
英文摘要:
      To meet the requirement of developing the new method for evaluating forecast skill of heavy rainfall, the main factors effecting forecasters` confidence of heavy rainstorm`s forecasting, that is, the forecasting ability of the statistics characteristics of the heavy rainstorm climate , the characteristics of movement scale of the effecting system and the numerical model. This paper designed a new mathematical model of Synthetic Predictability Index of Heavy Rain(SPI) which is composed of three components: rainstorm climate frequency, rainstorm area ratio and numerical model rainstorm forecasting success index (Threat Score, TS) via the use of 5km * 5km resolution muti-source precipitation fusion grid analysis data, site precipitation observation data and precipitation forecasting data of national region operational model and statistics method of extended space rainstorm sample of the National Meteorological Information Center from April to October in 2008 to 2016, to analysis the temporal and spatial variation and distribution characteristics of SPI. The results show that the regional precipitation of the regional heavy rainfall can be changed along seasons and its spatial distribution, but not uniformly: April to May, the more predictable areas are mainly distributed in southern China. From June to July, the main distribution in middle-July to August, large values center moved from northern part of Jianghuai to north and northeast regions. In September, the subtropical high pressure was withdrawn and large value center was moved south correspondingly. In addition, the partial correlation coefficient between rainstorm predictability index and three components shows that the partial correlation coefficient between the RA and the storm area ratio is the highest, and the partial correlation coefficient is higher than 0.9. The comprehensive index of rainstorm predictability in China has laid a footstone for the development of predictability based predictive rating of rainstorm forecasting.
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